Finally the latent space is used to control the so called channel attention mechanism, which is inspired by the attention used in NLP and vision models such as transformers [1]}, [2]}, [3]}.
The latent space is converted to a multiplicative effect on the output of the individual channels.
This approach allows to inject global information derived from the latent space, into the bias-preserving convolutional blocks.
Intuitively, it allows to reduce the importance of given spatial actions based on the global context.
